Overview

Dataset statistics

Number of variables6
Number of observations290618
Missing cells0
Missing cells (%)0.0%
Total size in memory35.4 MiB
Average record size in memory127.6 B

Variable types

Text3
Numeric2
DateTime1

Dataset

Description[unitless] Sensor that periodically collects information about the cellular networks (name, id, type) the smartphone is connected to. To compare each sensor observation, the frequency was reduced to one minute. The first non-missing name is reported for each of the categorical variables.
CreatorAndrea Bontempelli, Matteo Busso, Roy Alia Asiku
AuthorAndrea Bontempelli, Matteo Busso, Fausto Giunchiglia
URL
Copyright(c) University of Trento - Knowledge Diversity 2023

Variable descriptions

experimentidExperiment Id
useridUser id
timestampshow month(2), day(2), hour(2), minute(2), second(2), decimals(3)
cellidThe cell id
dbm(DeciBel-Milliwatts)The received signal strength.
typeThe technology type of the network (lte, wcdma, gsm, etc…)

Alerts

experimentid has constant value "wenetDenmark"Constant
dbm is highly skewed (γ1 = 23.17735007)Skewed
userid has 29840 (10.3%) zerosZeros

Reproduction

Analysis started2024-11-23 02:34:20.810686
Analysis finished2024-11-23 02:34:22.650984
Duration1.84 second
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

experimentid
Text

CONSTANT 

Experiment Id

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.5 MiB
2024-11-23T03:34:22.788167image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters3487416
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowwenetDenmark
2nd rowwenetDenmark
3rd rowwenetDenmark
4th rowwenetDenmark
5th rowwenetDenmark
ValueCountFrequency (%)
wenetdenmark 290618
100.0%
2024-11-23T03:34:22.998984image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 871854
25.0%
n 581236
16.7%
w 290618
 
8.3%
t 290618
 
8.3%
D 290618
 
8.3%
m 290618
 
8.3%
a 290618
 
8.3%
r 290618
 
8.3%
k 290618
 
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3487416
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 871854
25.0%
n 581236
16.7%
w 290618
 
8.3%
t 290618
 
8.3%
D 290618
 
8.3%
m 290618
 
8.3%
a 290618
 
8.3%
r 290618
 
8.3%
k 290618
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3487416
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 871854
25.0%
n 581236
16.7%
w 290618
 
8.3%
t 290618
 
8.3%
D 290618
 
8.3%
m 290618
 
8.3%
a 290618
 
8.3%
r 290618
 
8.3%
k 290618
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3487416
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 871854
25.0%
n 581236
16.7%
w 290618
 
8.3%
t 290618
 
8.3%
D 290618
 
8.3%
m 290618
 
8.3%
a 290618
 
8.3%
r 290618
 
8.3%
k 290618
 
8.3%

userid
Real number (ℝ)

ZEROS 

User id

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.27714044
Minimum0
Maximum27
Zeros29840
Zeros (%)10.3%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2024-11-23T03:34:23.104716image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median6
Q323
95-th percentile26
Maximum27
Range27
Interquartile range (IQR)20

Descriptive statistics

Standard deviation9.847369968
Coefficient of variation (CV)0.8732151579
Kurtosis-1.629274916
Mean11.27714044
Median Absolute Deviation (MAD)6
Skewness0.3511866412
Sum3277340
Variance96.9706953
MonotonicityIncreasing
2024-11-23T03:34:23.200802image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
3 50197
17.3%
6 46982
16.2%
23 43375
14.9%
2 38469
13.2%
0 29840
10.3%
26 29309
10.1%
17 19396
 
6.7%
20 14536
 
5.0%
25 8638
 
3.0%
18 3443
 
1.2%
Other values (7) 6433
 
2.2%
ValueCountFrequency (%)
0 29840
10.3%
2 38469
13.2%
3 50197
17.3%
6 46982
16.2%
8 1476
 
0.5%
ValueCountFrequency (%)
27 1336
 
0.5%
26 29309
10.1%
25 8638
 
3.0%
23 43375
14.9%
22 211
 
0.1%

timestamp
Date

show month(2), day(2), hour(2), minute(2), second(2), decimals(3)

Distinct290610
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
Minimum2020-11-16 07:00:00.391000
Maximum2020-12-11 21:59:42.994000
2024-11-23T03:34:23.313132image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-23T03:34:23.432534image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

cellid
Text

The cell id

Distinct1576
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size20.0 MiB
2024-11-23T03:34:23.559170image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length64
Median length64
Mean length64
Min length64

Characters and Unicode

Total characters18599552
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique629 ?
Unique (%)0.2%

Sample

1st row44e5ce649ed7651f28a9cb5e544db44ec24024a88f573191f30679bf62eb0220
2nd row44e5ce649ed7651f28a9cb5e544db44ec24024a88f573191f30679bf62eb0220
3rd row44e5ce649ed7651f28a9cb5e544db44ec24024a88f573191f30679bf62eb0220
4th row44e5ce649ed7651f28a9cb5e544db44ec24024a88f573191f30679bf62eb0220
5th row44e5ce649ed7651f28a9cb5e544db44ec24024a88f573191f30679bf62eb0220
ValueCountFrequency (%)
577d62dbdbd880d5a686c8f5a444a6a02d197f8197896b64428177e632006377 198202
68.2%
44e5ce649ed7651f28a9cb5e544db44ec24024a88f573191f30679bf62eb0220 37236
 
12.8%
fbfe7b357545b694b593418c0252cd12f226714e7062daefbd7f940860260fa2 4377
 
1.5%
0ea3a3e82671bf011f353c0fd7e18b33dc5be36f455628ade0b112499c207dee 4374
 
1.5%
ebb91593d8fa7dec356cb2ae99d305b92ad42b21cf14e61dfb45483591040eb8 3766
 
1.3%
07f849465564f588324a6e67562fe2ecc53fc8b7741a1616a019e54b7c053eef 2752
 
0.9%
9ecc0bc4a79c0ae2c59a2b6c066f016b98f3e6830a90982e70de95886c99ada9 2177
 
0.7%
bbc08f7dfa3ab56176b4b7e12c51a16d33c140f6caca26baaaf301b14b240f18 2162
 
0.7%
1ecb31ff9603ac95499399a242e0c51714b57a6edcf8ad1186634819d042d593 1829
 
0.6%
249243aae618c6531967517bdf2283e5615d8733e3164b21b427cf7f1b488020 1818
 
0.6%
Other values (1566) 31925
 
11.0%
2024-11-23T03:34:23.787271image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 1975072
10.6%
7 1904425
10.2%
8 1709602
 
9.2%
4 1548399
 
8.3%
d 1459968
 
7.8%
2 1241700
 
6.7%
0 1164455
 
6.3%
a 1072561
 
5.8%
5 1000222
 
5.4%
b 973662
 
5.2%
Other values (6) 4549486
24.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18599552
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
6 1975072
10.6%
7 1904425
10.2%
8 1709602
 
9.2%
4 1548399
 
8.3%
d 1459968
 
7.8%
2 1241700
 
6.7%
0 1164455
 
6.3%
a 1072561
 
5.8%
5 1000222
 
5.4%
b 973662
 
5.2%
Other values (6) 4549486
24.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18599552
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
6 1975072
10.6%
7 1904425
10.2%
8 1709602
 
9.2%
4 1548399
 
8.3%
d 1459968
 
7.8%
2 1241700
 
6.7%
0 1164455
 
6.3%
a 1072561
 
5.8%
5 1000222
 
5.4%
b 973662
 
5.2%
Other values (6) 4549486
24.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18599552
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
6 1975072
10.6%
7 1904425
10.2%
8 1709602
 
9.2%
4 1548399
 
8.3%
d 1459968
 
7.8%
2 1241700
 
6.7%
0 1164455
 
6.3%
a 1072561
 
5.8%
5 1000222
 
5.4%
b 973662
 
5.2%
Other values (6) 4549486
24.5%

dbm
Real number (ℝ)

SKEWED 

(DeciBel-Milliwatts)The received signal strength.

Distinct98
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3975377.983
Minimum-141
Maximum2147483647
Zeros23
Zeros (%)< 0.1%
Negative290057
Negative (%)99.8%
Memory size2.2 MiB
2024-11-23T03:34:23.920522image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-141
5-th percentile-120
Q1-113
median-106
Q3-93
95-th percentile-75
Maximum2147483647
Range2147483788
Interquartile range (IQR)20

Descriptive statistics

Standard deviation92311998.84
Coefficient of variation (CV)23.22093628
Kurtosis535.1932392
Mean3975377.983
Median Absolute Deviation (MAD)9
Skewness23.17735007
Sum1.155316399 × 1012
Variance8.52150513 × 1015
MonotonicityNot monotonic
2024-11-23T03:34:24.042833image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-113 60333
20.8%
-120 28310
 
9.7%
-97 8934
 
3.1%
-101 8258
 
2.8%
-103 7962
 
2.7%
-109 7747
 
2.7%
-107 7622
 
2.6%
-105 7558
 
2.6%
-111 7324
 
2.5%
-95 7284
 
2.5%
Other values (88) 139286
47.9%
ValueCountFrequency (%)
-141 1
 
< 0.1%
-140 236
0.1%
-139 51
 
< 0.1%
-138 31
 
< 0.1%
-137 48
 
< 0.1%
ValueCountFrequency (%)
2147483647 538
0.2%
0 23
 
< 0.1%
-40 1
 
< 0.1%
-47 1
 
< 0.1%
-48 12
 
< 0.1%

type
Text

The technology type of the network (lte, wcdma, gsm, etc…)

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.2 MiB
2024-11-23T03:34:24.122482image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.638542692
Min length3

Characters and Unicode

Total characters1057426
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowlte
2nd rowlte
3rd rowlte
4th rowlte
5th rowlte
ValueCountFrequency (%)
lte 132328
45.5%
wcdma 92786
31.9%
gsm 65504
22.5%
2024-11-23T03:34:24.315810image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
m 158290
15.0%
l 132328
12.5%
t 132328
12.5%
e 132328
12.5%
w 92786
8.8%
c 92786
8.8%
d 92786
8.8%
a 92786
8.8%
g 65504
6.2%
s 65504
6.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1057426
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
m 158290
15.0%
l 132328
12.5%
t 132328
12.5%
e 132328
12.5%
w 92786
8.8%
c 92786
8.8%
d 92786
8.8%
a 92786
8.8%
g 65504
6.2%
s 65504
6.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1057426
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
m 158290
15.0%
l 132328
12.5%
t 132328
12.5%
e 132328
12.5%
w 92786
8.8%
c 92786
8.8%
d 92786
8.8%
a 92786
8.8%
g 65504
6.2%
s 65504
6.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1057426
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
m 158290
15.0%
l 132328
12.5%
t 132328
12.5%
e 132328
12.5%
w 92786
8.8%
c 92786
8.8%
d 92786
8.8%
a 92786
8.8%
g 65504
6.2%
s 65504
6.2%

Correlations

2024-11-23T03:34:24.392031image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
dbmuserid
dbm1.0000.065
userid0.0651.000